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About Our Group

Our research focuses on methods to enable engineering design and analysis with intrusive and non-intrusive data-driven surrogate models.

Simulating parameterized systems of equations is ubiquitous in science and engineering. It is often the case that solving such systems with high level of accuracy is a computationally intensive process. For many-query analyses such as uncertainty quantification and optimization, reduced models are required to make the analysis tractable. Model reduction is a broad and active field. Several techniques exist, but there is no such thing as “one method to rule them all”.

Recent Posts

• Efficient Sampling Methods for Machine Learning Error Models with application to Surrogates of Steady Hypersonic Flows
  Elizabeth H. Krath, David S. Ching and Patrick J. Blonigan AIAA 2022-1249 This paper presents an investigation into sampling strategies for reducing the computational expense of creating error models for steady hypersonic flow surrogate models. The […]
• Projection-Based Model Reduction for Coupled Conduction-Enclosure Radiation Systems
  Victor E Brunini, Eric Parish, John Tencer, and Francesco Rizzi ASME Journal of Heat Transfer A projection-based reduced order model (pROM) methodology has been developed for transient heat transfer problems involving coupled conduction […]
• A Compute-Bound Formulation of Galerkin Model Reduction for Linear Time-Invariant Dynamical Systems
  Francesco Rizzi, Eric Parish, Patrick Blonigan, and John Tencer Computer Methods in Applied Mechanics and Engineering, 384(1) This work aims to advance computational methods for projection-based reduced order models (ROMs) of linear time-invariant […]
• Model Reduction of Hypersonic Aerodynamics with Residual Minimization Techniques
  David S. Ching, Patrick J. Blonigan, Francesco Rizzi and Jeffrey A. Fike AIAA 2022-1247 High-fidelity hypersonic aerodynamic simulations require extensive computational resources, hindering their usage in hypersonic vehicle design and uncertainty quantification. Projection-based reduced-order models (ROMs) are […]
• Postdoctoral Appointee in Computational Science for Reduced Order and/or Surrogate Modeling
  Location: Livermore, CAFull‐time/Temporary We are seeking a Postdoctoral Appointee at Sandia National Laboratories to join ourdepartment! This position involves research on surrogate and reduced order modelingwith an eye towards developing methods that can […]